Advertisement

Stacked Denoising Autoencoders for Face Pose Normalization

  • Yoonseop Kang
  • Kang-Tae Lee
  • Jihyun Eun
  • Sung Eun Park
  • Seungjin Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

The performance of face recognition systems are significantly degraded by the pose variations of face images. In this paper, a global pose normalization method is proposed for pose-invariant face recognition. The proposed method uses a deep network to convert non-frontal face images into frontal face images. Unlike existing part-based methods that require complex appearence models or multiple face part detectors, the proposed method relies only on a face detector. The experimental results using the Georgia tech face database demonstrate the advantages of the proposed method.

Keywords

Pose normalization face recognition autoencoder stacked denoising autoencoder 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Du, S., Ward, R.: Component-wise pose normalization for pose-invariant face recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 873–876 (2009)Google Scholar
  2. 2.
    Asthana, A., Marks, T.K., Jones, M.J., Tieu, K.H., Rohith, M.V.: Fully automatic pose-invariant face recognition via 3D pose normalization. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 937–944 (2011)Google Scholar
  3. 3.
    Cootes, T., Walker, K., Talyor, C.J.: View-based active appearance models. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 227–232 (2000)Google Scholar
  4. 4.
    Becker, S.: Unsupervised learning procedures for neural networks. The International Journal of Neural Systems 1 & 2, 17–33 (1991)CrossRefGoogle Scholar
  5. 5.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 1096–1103 (2008)Google Scholar
  6. 6.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Nefian, A.V.: Georgia tech face database (1999), http://www.anefian.com/research/face_reco.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yoonseop Kang
    • 1
  • Kang-Tae Lee
    • 2
  • Jihyun Eun
    • 2
  • Sung Eun Park
    • 2
  • Seungjin Choi
    • 1
  1. 1.Department of Computer Science and EngineeringPohang University of Science and TechnologyPohangKorea
  2. 2.KT Advanced Institute of TechnologySeoulKorea

Personalised recommendations